Sign determination methods for the respiratory signal in data-driven PET gating

Patient respiratory motion during PET image acquisition leads to blurring in the reconstructed images and may cause significant artifacts, resulting in decreased lesion detectability, inaccurate standard uptake value calculation and incorrect treatment planning in radiation therapy. To reduce these effects data can be regrouped into (nearly) 'motion-free' gates prior to reconstruction by selecting the events with respect to the breathing phase. This gating procedure therefore needs a respiratory signal: on current scanners it is obtained from an external device, whereas with data driven (DD) methods it can be directly obtained from the raw PET data. DD methods thus eliminate the use of external equipment, which is often expensive, needs prior setup and can cause patient discomfort, and they could also potentially provide increased fidelity to the internal movement. DD methods have been recently applied on PET data showing promising results. However, many methods provide signals whose direction with respect to the physical motion is uncertain (i.e. their sign is arbitrary), therefore a maximum in the signal could refer either to the end-inspiration or end-expiration phase, possibly causing inaccurate motion correction. In this work we propose two novel methods, CorrWeights and CorrSino, to detect the correct direction of the motion represented by the DD signal, that is obtained by applying principal component analysis (PCA) on the acquired data. They only require the PET raw data, and they rely on the assumption that one of the major causes of change in the acquired data related to the chest is respiratory motion in the axial direction, that generates a cranio-caudal motion of the internal organs. We also implemented two versions of a published registration-based method, that require image reconstruction. The methods were first applied on XCAT simulations, and later evaluated on cancer patient datasets monitored by the Varian Real-time Position ManagementTM (RPM) device, selecting the lower chest bed positions. For each patient different time intervals were evaluated ranging from 50 to 300 s in duration. The novel methods proved to be generally more accurate than the registration-based ones in detecting the correct sign of the respiratory signal, and their failure rates are lower than 3% when the DD signal is highly correlated with the RPM. They also have the advantage of faster computation time, avoiding reconstruction. Moreover, CorrWeights is not specifically related to PCA and considering its simple implementation, it could easily be applied together with any DD method in clinical practice

Penalized PET/CT Reconstruction Algorithms With Automatic Realignment for Anatomical Priors

Two algorithms for solving misalignment issues in penalized PET/CT reconstruction using anatomical priors are proposed. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to align them with the measured data. Our current implementation alternates between alignment estimation and image reconstruction. We have chosen parallel level sets (PLSs) as a representative anatomical penalty, incorporating a spatially variant penalty strength. The performance was evaluated using simulated nontime-of-flight data generated with an XCAT phantom in the thorax region. We used the attenuation map in the anatomical prior. The results demonstrated that both methods can estimate the misalignment and deform the anatomical image accordingly. However, the performance of the first approach depends highly on the workflow of the alternating process. The second approach shows a faster convergence rate to the correct alignment and is less sensitive to the workflow. The presence of anatomical information improves the convergence rate of misalignment estimation for the second approach but slow it down for the first approach. Both approaches show improved performance in misalignment estimation as the data noise level decreases

Performance improvement and validation of a new MAP reconstruction algorithm

We previously proposed a fast maximum a posteriori (MAP) algorithm, limited-memory Broyden-Fletcher-Goldfarb- Shanno with boundary constrains (LBFGS-B-PC), combining LBFGS-B with diagonal preconditioning. Previous results have shown in simulations that it converges using around 40 projections independent of many factors. The aim of this study is to improve the algorithm further by using a better initial image and a modified preconditioner that is less sensitive to noise and data scale. By initializing the algorithm with the best initial image (one full iteration of OSEM with 35 subsets), ROI values can converge almost twice as fast for the same computation time. Moreover, the new preconditioner makes the performance more consistent between high and low count data sets. In addition, we have found a means to choose the stopping criteria to reach a desired level of quantitative accuracy in the reconstructed image. Based on the results with patient data, the optimized LBFGS-B-PC shows promise for clinical imaging

Algorithms for Solving Misalignment Issues in Penalized PET/CT Reconstruction Using Anatomical Priors

In dual-modality systems, using anatomical priors has been shown to improve image quality and quantification in emission tomography. However, alignment between the functional and anatomical images is crucial. In this study, we propose two algorithms for solving misalignment issues. Both approaches are based on a recently published joint motion estimation and image reconstruction method. The first approach deforms the anatomical image to align it with the functional one while the second approach deforms both images to align them with the measured data. Our current implementation uses alternates between image reconstruction and alignment estimation. To evaluate the potential of these approaches, we have chosen Parallel Level Sets (PLS) as a representative anatomical penalty since it has shown promising results in literature, incorporating a spatially-variant penalty strength to achieve uniform local contrast and fast convergence rate. The performance evaluation was achieved by using simulated non-TOF data generated with an XCAT phantom in the thorax region. We used the attenuation image in the anatomical prior. The results demonstrated that both methods are able to estimate the misalignment and deform the anatomical image accordingly when a proper workflow for the alternating optimization is applied. However, the performance of the first approach depends highly on the workflow of the alternating process. In contrast, the second approach shows the ability to converge to the correct alignment faster than the first approach does, independent of the workflow. Our results indicate that it is possible to align functional and anatomical information, enabling the use of anatomical priors in practice

Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning

OAPA This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden- Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGSB), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGSB and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three 18F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGSB- PC shows promise for clinical application

Recent Progress in STIR 5.0

STIR is an open source software for Emission Tomography data manipulation and image reconstruction, covering both PET and SPECT. In this work recent additions to the STIR code base are highlighted, namely the ability to read General Electric (GE) Raw Data Format 9 (RDF9) files, incorporation of GPU operators for forward and back projection, as well as work towards quantitative imaging for both PET and SPECT

PET/CT Respiratory Motion Correction With a Single Attenuation Map Using NAC Derived Deformation Fields

Respiratory motion correction is beneficial in positron emission tomography. Different strategies for handling attenuation correction in conjunction with motion correction exist. In clinical practice, usually a single attenuation map is available, derived from computed tomography in one respiratory state. This can introduce an unwanted bias (through misaligned anatomy) into the motion correction algorithm. This paper builds upon previous work which suggested that non-attenuation corrected data was suitable for motion estimation, through the use of motion models, if time-of-flight data are available. Here, the previous work is expanded upon by incorporating attenuation correction in an iterative process. Non-attenuation corrected volumes are reconstructed using ordered subset expectation maximisation and used as input for motion model estimation. A single attenuation map is then warped to the volumes, using the motion model, the volumes are attenuation corrected, after which another motion estimation and correction cycle is performed. For validation, 4-Dimensional Extended Cardiac Torso simulations are used, for one bed position, with a field of view including the base of the lungs and the diaphragm. The output from the proposed method is evaluated against a non-motion corrected reconstruction of the same data visually, using a profile as well as standardised uptake value analysis. Results indicate that motion correction of inter-respiratory cycle motion is possible with this method, while accounting for attenuation deformatio

Improvement of the Sign Determination Method for Data-Driven respiratory signal in TOF-PET

Respiratory gating and motion correction can increase resolution in PET chest imaging, but require a respiratory signal. Data-Driven (DD) methods aim to produce a respiratory signal from PET data, avoiding the use of external devices. Principal Component Analysis (PCA) is an easy to implement DD algorithm whose signals, however, are determined up to an arbitrary factor. The direction of the motion represented by its signal has to be determined. In this work we present the extension to TOF data of a previously presented sign-determination method. Furthermore, we propose the application of a selection process in sinogram space, to automatically select the areas of the data mostly affected by respiratory motion. The performance of the updated sign-determination method is evaluated on patient data, and the effect of TOF information and masking process is investigated also in terms of quality of the PCA respiratory signal

Data Driven Respiratory Signal Detection in PET Taking Advantage of Time-of-Flight Data

Respiratory gating is a powerful tool for tackling motion-related issues in chest PET imaging. On current scanners the respiratory signal is obtained from external devices, whereas with Data-Driven methods it can be extracted directly from the data. The aim of this work is to show the increased potential of the application of Principal Component Analysis (PCA) on TOF data. We propose a methodology that retains the TOF information and compare it to the non-TOF method. We tested the method on 16 FDG oncology patients, monitored with an RPM camera. To further investigate the benefit of TOF, PCA was selectively applied to sets of TOF bins equidistant from the center. The correlation with the RPM, the level of noise and the respiratory-likeness were analysed for all the obtained respiratory signals. The results of our analysis showed that retaining the TOF information into the sinograms considerably increased the quality of the extracted respiratory signals